§ ~Wêh ãóT—dZddlZddlmZddlmZed¦«dd„¦«ZdS) z!MNIST handwritten digits dataset.éN)Úget_file)Ú keras_exportzkeras.datasets.mnist.load_dataú mnist.npzcóà—d}t||dzd¬¦«}tj|d¬¦«5}|d|d}}|d |d }}||f||ffcd d d ¦«S#1swxYwYd S) aDLoads the MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. More info can be found at the [MNIST homepage](http://yann.lecun.com/exdb/mnist/). Args: path: path where to cache the dataset locally (relative to `~/.keras/datasets`). Returns: Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`. **x_train**: uint8 NumPy array of grayscale image data with shapes `(60000, 28, 28)`, containing the training data. Pixel values range from 0 to 255. **y_train**: uint8 NumPy array of digit labels (integers in range 0-9) with shape `(60000,)` for the training data. **x_test**: uint8 NumPy array of grayscale image data with shapes (10000, 28, 28), containing the test data. Pixel values range from 0 to 255. **y_test**: uint8 NumPy array of digit labels (integers in range 0-9) with shape `(10000,)` for the test data. Example: ```python (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() assert x_train.shape == (60000, 28, 28) assert x_test.shape == (10000, 28, 28) assert y_train.shape == (60000,) assert y_test.shape == (10000,) ``` License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the [Creative Commons Attribution-Share Alike 3.0 license.]( https://creativecommons.org/licenses/by-sa/3.0/) zrsuðð(Ð'àÐÐÐà+Ð+Ð+Ð+Ð+Ð+ð:Ð9Ð9Ð9Ð9Ð9ð€Ð.Ñ/Ô/ð<4ð<4ð<4ñ0Ô/ð<4ð<4ð<4r